Learning-based data analytics: Moving towards transparent power grids

Abstract

In this paper, we present the learning-based data analytics moving towards transparent power grids and provide some possible extensions including machine learning, big data analytics, and knowledge transferring. The closed loops of data and knowledge are illustrated and the challenges for establishing the closed loops are discussed. General ideas and recent developments in supervised learning, unsupervised learning, and reinforcement learning are presented together with extensions for power system applications. Furthermore, much emphasis is placed on privacy-preserving data analysis, transfer of knowledge, machine learning for causal inference, scalability and flexibility of data analytics, and efficiency and reliability of computation. Existing integrated solutions in the industry featuring the Industrial Internet and the digital grid are also introduced.